A Novel Tree Biomass Estimation Model Applying the Pipe Model Theory and Adaptable to UAV-Derived Canopy Height Models

Aiming to develop a new tree biomass estimation model that is adaptable to airborne observations of forest canopies by unmanned aerial vehicles (UAVs), we applied two theories of plant form; the pipe model theory (PMT) and the statical model of plant form as an extension of the PMT for tall trees. Based on these theories, tree biomass was formulated using an individual tree canopy height model derived from a UAV. The advantage of this model is that it does not depend on diameter at breast height which is difficult to observe using remote-sensing techniques. We also proposed a treetop detection method based on the fractal geometry of the crown and stand. Comparing surveys in plantations of Japanese cedar (Cryptomeria japonica D. Don) and Japanese cypress (Chamaecyparis obtusa Endl.) in Japan, the root mean square error (RMSE) of the estimated stem volume was 0.26 m3 and was smaller than or comparative to that of models using different methodologies. The significance of this model is that it contains only one empirical parameter to be adjusted which was found to be rather stable among different species and sites, suggesting the wide adaptability of the model. Finally, we demonstrated the potential applicability of the model to light detection and ranging (LiDAR) data which can provide vertical leaf density distribution.

[1]  Johannes Breidenbach,et al.  Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data? , 2020, Remote. Sens..

[2]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

[3]  Randolph H. Wynne,et al.  Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA , 2004, Forest Science.

[4]  T. Kira,et al.  A QUANTITATIVE ANALYSIS OF PLANT FORM-THE PIPE MODEL THEORY : II. FURTHER EVIDENCE OF THE THEORY AND ITS APPLICATION IN FOREST ECOLOGY , 1964 .

[5]  Adrián Cardil,et al.  Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System , 2020, Remote. Sens..

[6]  H. Pretzsch.  Species-specific allometric scaling under self-thinning: evidence from long-term plots in forest stands , 2005, Oecologia.

[7]  V. Lieffers,et al.  Crown Cover Is Correlated with Relative Density, Tree Slenderness, and Tree Height in Lodgepole Pine , 2004, Forest Science.

[8]  Masayuki Itoh,et al.  Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest , 2017, Remote. Sens..

[9]  Pierre Dutilleul,et al.  Advances in the implementation of the box-counting method of fractal dimension estimation , 1999, Appl. Math. Comput..

[10]  Ashok Samal,et al.  A Method for Estimating Fractal Dimension of Tree Crowns from Digital Images , 2007, Int. J. Pattern Recognit. Artif. Intell..

[11]  J. Phattaralerphong,et al.  A method for 3D reconstruction of tree crown volume from photographs: assessment with 3D-digitized plants. , 2005, Tree physiology.

[12]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[13]  Andrea Berton,et al.  Forestry applications of UAVs in Europe: a review , 2017 .

[14]  K. Niklas,et al.  A predictive nondestructive model for the covariation of tree height, diameter, and stem volume scaling relationships , 2016, Scientific Reports.

[15]  Jan-Peter Mund,et al.  UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring , 2019, Remote. Sens..

[16]  Xin Shen,et al.  Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests , 2019, Forests.

[17]  Juha Hyyppä,et al.  Autonomous Collection of Forest Field Reference - The Outlook and a First Step with UAV Laser Scanning , 2017, Remote. Sens..

[18]  R. Beyer,et al.  The pipe model theory half a century on: a review , 2018, Annals of botany.

[19]  M. Monsi.  Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion , 1953 .

[20]  Nicholas C. Coops,et al.  lidR: An R package for analysis of Airborne Laser Scanning (ALS) data , 2020 .

[21]  Naoyuki Furuya,et al.  Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests , 2020, Remote. Sens..

[22]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[23]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[24]  D. Morton,et al.  Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion , 2018 .

[25]  Jacek P. Siry,et al.  Sustainable forest management: global trends and opportunities , 2005 .

[26]  Estimating forest resources using airbone LiDAR , 2008 .

[27]  Geoffrey G. Parker,et al.  The canopy surface and stand development: assessing forest canopy structure and complexity with near-surface altimetry , 2004 .

[28]  T. Yamakura,et al.  Fractal nature of spatial patterns in Japanese evergreen oak forest trees , 1999 .

[29]  K. Nackaerts,et al.  A fractal dimension-based modelling approach for studying the effect of leaf distribution on LAI retrieval in forest canopies , 2006 .

[30]  Jinqiang Cui,et al.  UAV LiDAR for below-canopy forest surveys , 2013 .

[31]  B. Zeide,et al.  Fractal dimensions of tree crowns in three loblolly pine plantations of coastal South Carolina , 1991 .

[32]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[33]  Johnce E. Hall,et al.  Global progress toward sustainable forest management , 2015 .

[34]  Arko Lucieer,et al.  Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Juha Hyyppä,et al.  Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms , 2016, Remote. Sens..

[36]  Kichiro Shinozaki,et al.  A STATICAL MODEL OF PLANT FORM-FURTHER ANALYSIS OF THE PIPE MODEL THEORY , 1979 .

[37]  Boris Zeide,et al.  Fractal geometry in forestry applications , 1991 .

[38]  A. Mäkelä,et al.  Self-shading affects allometric scaling in trees , 2010 .

[39]  Lin Cao,et al.  Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[40]  Lammert Kooistra,et al.  Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR , 2017, Sensors.

[41]  L. Wallace,et al.  Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds , 2016 .

[42]  Fujimoto,et al.  An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation , 2019, Forests.

[43]  T. Kira,et al.  A QUANTITATIVE ANALYSIS OF PLANT FORM-THE PIPE MODEL THEORY : I.BASIC ANALYSES , 1964 .

[44]  Xin Shen,et al.  Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests , 2019, Remote. Sens..